#include "utils.hpp"

#include "common.h"
#include "grammar-parser.h"
#include "llama.h"

#include "nlohmann/json.hpp"

#include <atomic>
#include <chrono>
#include <condition_variable>
#include <cstddef>
#include <memory>
#include <mutex>
#include <set>
#include <signal.h>
#include <thread>

using json = nlohmann::ordered_json;

enum stop_type
{
    STOP_TYPE_FULL,
    STOP_TYPE_PARTIAL,
};

enum slot_state
{
    SLOT_STATE_IDLE,
    SLOT_STATE_PROCESSING,
};

enum slot_command
{
    SLOT_COMMAND_NONE,
    SLOT_COMMAND_LOAD_PROMPT,
    SLOT_COMMAND_RELEASE,
};

enum server_state
{
    SERVER_STATE_LOADING_MODEL, // Server is starting up, model not fully loaded yet
    SERVER_STATE_READY,         // Server is ready and model is loaded
    SERVER_STATE_ERROR          // An error occurred, load_model failed
};

enum server_task_type
{
    SERVER_TASK_TYPE_COMPLETION,
    SERVER_TASK_TYPE_CANCEL,
    SERVER_TASK_TYPE_NEXT_RESPONSE,
    SERVER_TASK_TYPE_METRICS,
    SERVER_TASK_TYPE_SLOT_SAVE,
    SERVER_TASK_TYPE_SLOT_RESTORE,
    SERVER_TASK_TYPE_SLOT_ERASE,
};

struct server_task
{
    int id = -1; // to be filled by server_queue
    int id_multi = -1;
    int id_target = -1;

    server_task_type type;
    json data;

    bool infill = false;
    bool embedding = false;
};

struct server_task_result
{
    int id = -1;
    int id_multi = -1;

    json data;

    bool stop;
    bool error;
};

struct server_task_multi
{
    int id = -1;

    std::set<int> subtasks_remaining;
    std::vector<server_task_result> results;
};

struct slot_params
{
    bool stream = true;
    bool cache_prompt = false; // remember the prompt to avoid reprocessing all prompt

    int32_t n_keep = 0; // number of tokens to keep from initial prompt
    int32_t n_discard =
        0; // number of tokens after n_keep that may be discarded when shifting context, 0 defaults to half
    int32_t n_predict = -1; // new tokens to predict

    std::vector<std::string> antiprompt;

    json input_prefix;
    json input_suffix;
};

struct server_slot
{
    int id;
    int id_task = -1;
    int id_multi = -1;

    struct slot_params params;

    slot_state state = SLOT_STATE_IDLE;
    slot_command command = SLOT_COMMAND_NONE;

    // used to determine the slot that has been used the longest
    int64_t t_last_used = -1;

    // generation props
    int32_t n_ctx = 0; // context size per slot
    int32_t n_past = 0;
    int32_t n_decoded = 0;
    int32_t n_remaining = -1;
    int32_t i_batch = -1;
    int32_t n_predict = -1; // TODO: disambiguate from params.n_predict

    int32_t n_prompt_tokens = 0;
    int32_t n_prompt_tokens_processed = 0;

    json prompt;

    // when a task is submitted, we first tokenize the prompt and store it here
    std::vector<llama_token> prompt_tokens;

    std::string generated_text;
    std::vector<llama_token> cache_tokens;
    std::vector<completion_token_output> generated_token_probs;

    bool infill = false;
    bool embedding = false;
    bool has_next_token = true;
    bool truncated = false;
    bool stopped_eos = false;
    bool stopped_word = false;
    bool stopped_limit = false;

    bool oaicompat = false;

    std::string oaicompat_model;
    std::string stopping_word;

    // sampling
    llama_token sampled;
    struct llama_sampling_params sparams;
    llama_sampling_context *ctx_sampling = nullptr;
    json json_schema;

    int32_t ga_i = 0;   // group-attention state
    int32_t ga_n = 1;   // group-attention factor
    int32_t ga_w = 512; // group-attention width

    int32_t n_past_se = 0; // self-extend

    // stats
    size_t n_sent_text = 0; // number of sent text character
    size_t n_sent_token_probs = 0;

    int64_t t_start_process_prompt;
    int64_t t_start_generation;

    double t_prompt_processing; // ms
    double t_token_generation;  // ms

    void reset()
    {
        n_prompt_tokens = 0;
        generated_text = "";
        truncated = false;
        stopped_eos = false;
        stopped_word = false;
        stopped_limit = false;
        stopping_word = "";
        n_past = 0;
        n_sent_text = 0;
        n_sent_token_probs = 0;
        infill = false;
        ga_i = 0;
        n_past_se = 0;

        generated_token_probs.clear();
    }

    bool has_budget(gpt_params &global_params)
    {
        if (params.n_predict == -1 && global_params.n_predict == -1)
        {
            return true; // limitless
        }

        n_remaining = -1;

        if (params.n_predict != -1)
        {
            n_remaining = params.n_predict - n_decoded;
        }
        else if (global_params.n_predict != -1)
        {
            n_remaining = global_params.n_predict - n_decoded;
        }

        return n_remaining > 0; // no budget
    }

    bool available() const
    {
        return state == SLOT_STATE_IDLE && command == SLOT_COMMAND_NONE;
    }

    bool is_processing() const
    {
        return (state == SLOT_STATE_IDLE && command == SLOT_COMMAND_LOAD_PROMPT) || state == SLOT_STATE_PROCESSING;
    }

    void add_token_string(const completion_token_output &token)
    {
        if (command == SLOT_COMMAND_RELEASE)
        {
            return;
        }
        generated_token_probs.push_back(token);
    }

    void release()
    {
        if (state == SLOT_STATE_PROCESSING)
        {
            t_token_generation = (ggml_time_us() - t_start_generation) / 1e3;
            command = SLOT_COMMAND_RELEASE;
        }
    }

    json get_formated_timings() const
    {
        return json{
            {"prompt_n", n_prompt_tokens_processed},
            {"prompt_ms", t_prompt_processing},
            {"prompt_per_token_ms", t_prompt_processing / n_prompt_tokens_processed},
            {"prompt_per_second", 1e3 / t_prompt_processing * n_prompt_tokens_processed},

            {"predicted_n", n_decoded},
            {"predicted_ms", t_token_generation},
            {"predicted_per_token_ms", t_token_generation / n_decoded},
            {"predicted_per_second", 1e3 / t_token_generation * n_decoded},
        };
    }

    size_t find_stopping_strings(const std::string &text, const size_t last_token_size, const stop_type type)
    {
        size_t stop_pos = std::string::npos;

        for (const std::string &word : params.antiprompt)
        {
            size_t pos;

            if (type == STOP_TYPE_FULL)
            {
                const size_t tmp = word.size() + last_token_size;
                const size_t from_pos = text.size() > tmp ? text.size() - tmp : 0;

                pos = text.find(word, from_pos);
            }
            else
            {
                pos = find_partial_stop_string(word, text);
            }

            if (pos != std::string::npos && (stop_pos == std::string::npos || pos < stop_pos))
            {
                if (type == STOP_TYPE_FULL)
                {
                    stopped_word = true;
                    stopping_word = word;
                    has_next_token = false;
                }
                stop_pos = pos;
            }
        }

        return stop_pos;
    }

    void print_timings() const
    {
        char buffer[512];

        double t_token = t_prompt_processing / n_prompt_tokens_processed;
        double n_tokens_second = 1e3 / t_prompt_processing * n_prompt_tokens_processed;

        snprintf(buffer, 512,
                 "prompt eval time     = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)",
                 t_prompt_processing, n_prompt_tokens_processed, t_token, n_tokens_second);

        LOG_INFO(buffer, {
                             {"id_slot", id},
                             {"id_task", id_task},
                             {"t_prompt_processing", t_prompt_processing},
                             {"n_prompt_tokens_processed", n_prompt_tokens_processed},
                             {"t_token", t_token},
                             {"n_tokens_second", n_tokens_second},
                         });

        t_token = t_token_generation / n_decoded;
        n_tokens_second = 1e3 / t_token_generation * n_decoded;

        snprintf(buffer, 512,
                 "generation eval time = %10.2f ms / %5d runs   (%8.2f ms per token, %8.2f tokens per second)",
                 t_token_generation, n_decoded, t_token, n_tokens_second);

        LOG_INFO(buffer, {
                             {"id_slot", id},
                             {"id_task", id_task},
                             {"t_token_generation", t_token_generation},
                             {"n_decoded", n_decoded},
                             {"t_token", t_token},
                             {"n_tokens_second", n_tokens_second},
                         });

        snprintf(buffer, 512, "          total time = %10.2f ms", t_prompt_processing + t_token_generation);

        LOG_INFO(buffer, {
                             {"id_slot", id},
                             {"id_task", id_task},
                             {"t_prompt_processing", t_prompt_processing},
                             {"t_token_generation", t_token_generation},
                             {"t_total", t_prompt_processing + t_token_generation},
                         });
    }
};

struct server_metrics
{
    int64_t t_start = 0;

    uint64_t n_prompt_tokens_processed_total = 0;
    uint64_t t_prompt_processing_total = 0;
    uint64_t n_tokens_predicted_total = 0;
    uint64_t t_tokens_generation_total = 0;

    uint64_t n_prompt_tokens_processed = 0;
    uint64_t t_prompt_processing = 0;

    uint64_t n_tokens_predicted = 0;
    uint64_t t_tokens_generation = 0;

    void init()
    {
        t_start = ggml_time_us();
    }

    void on_prompt_eval(const server_slot &slot)
    {
        n_prompt_tokens_processed_total += slot.n_prompt_tokens_processed;
        n_prompt_tokens_processed += slot.n_prompt_tokens_processed;
        t_prompt_processing += slot.t_prompt_processing;
        t_prompt_processing_total += slot.t_prompt_processing;
    }

    void on_prediction(const server_slot &slot)
    {
        n_tokens_predicted_total += slot.n_decoded;
        n_tokens_predicted += slot.n_decoded;
        t_tokens_generation += slot.t_token_generation;
        t_tokens_generation_total += slot.t_token_generation;
    }

    void reset_bucket()
    {
        n_prompt_tokens_processed = 0;
        t_prompt_processing = 0;
        n_tokens_predicted = 0;
        t_tokens_generation = 0;
    }
};

struct server_queue
{
    int id = 0;
    bool running;

    // queues
    std::vector<server_task> queue_tasks;
    std::vector<server_task> queue_tasks_deferred;

    std::vector<server_task_multi> queue_multitasks;

    std::mutex mutex_tasks;
    std::condition_variable condition_tasks;

    // callback functions
    std::function<void(server_task &)> callback_new_task;
    std::function<void(server_task_multi &)> callback_finish_multitask;
    std::function<void(void)> callback_update_slots;

    // Add a new task to the end of the queue
    int post(server_task task)
    {
        std::unique_lock<std::mutex> lock(mutex_tasks);
        if (task.id == -1)
        {
            task.id = id++;
            LOG_VERBOSE("new task id", {{"new_id", task.id}});
        }
        queue_tasks.push_back(std::move(task));
        condition_tasks.notify_one();
        return task.id;
    }

    // Add a new task, but defer until one slot is available
    void defer(server_task task)
    {
        std::unique_lock<std::mutex> lock(mutex_tasks);
        queue_tasks_deferred.push_back(std::move(task));
    }

    // Get the next id for creating anew task
    int get_new_id()
    {
        std::unique_lock<std::mutex> lock(mutex_tasks);
        int new_id = id++;
        LOG_VERBOSE("new task id", {{"new_id", new_id}});
        return new_id;
    }

    // Register function to process a new task
    void on_new_task(std::function<void(server_task &)> callback)
    {
        callback_new_task = std::move(callback);
    }

    // Register function to process a multitask when it is finished
    void on_finish_multitask(std::function<void(server_task_multi &)> callback)
    {
        callback_finish_multitask = std::move(callback);
    }

    // Register the function to be called when all slots data is ready to be processed
    void on_update_slots(std::function<void(void)> callback)
    {
        callback_update_slots = std::move(callback);
    }

    // Call when the state of one slot is changed
    void notify_slot_changed()
    {
        // move deferred tasks back to main loop
        std::unique_lock<std::mutex> lock(mutex_tasks);
        for (auto &task : queue_tasks_deferred)
        {
            queue_tasks.push_back(std::move(task));
        }
        queue_tasks_deferred.clear();
    }

    // end the start_loop routine
    void terminate()
    {
        std::unique_lock<std::mutex> lock(mutex_tasks);
        running = false;
        condition_tasks.notify_all();
    }

    /**
     * Main loop consists of these steps:
     * - Wait until a new task arrives
     * - Process the task (i.e. maybe copy data into slot)
     * - Check if multitask is finished
     * - Update all slots
     */
    void start_loop()
    {
        running = true;

        while (true)
        {
            LOG_VERBOSE("new task may arrive", {});

            while (true)
            {
                std::unique_lock<std::mutex> lock(mutex_tasks);
                if (queue_tasks.empty())
                {
                    lock.unlock();
                    break;
                }
                server_task task = queue_tasks.front();
                queue_tasks.erase(queue_tasks.begin());
                lock.unlock();
                LOG_VERBOSE("callback_new_task", {{"id_task", task.id}});
                callback_new_task(task);
            }

            LOG_VERBOSE("update_multitasks", {});

            // check if we have any finished multitasks
            auto queue_iterator = queue_multitasks.begin();
            while (queue_iterator != queue_multitasks.end())
            {
                if (queue_iterator->subtasks_remaining.empty())
                {
                    // all subtasks done == multitask is done
                    server_task_multi current_multitask = *queue_iterator;
                    callback_finish_multitask(current_multitask);
                    // remove this multitask
                    queue_iterator = queue_multitasks.erase(queue_iterator);
                }
                else
                {
                    ++queue_iterator;
                }
            }

            // all tasks in the current loop is processed, slots data is now ready
            LOG_VERBOSE("callback_update_slots", {});

            callback_update_slots();

            LOG_VERBOSE("wait for new task", {});
            {
                std::unique_lock<std::mutex> lock(mutex_tasks);
                if (queue_tasks.empty())
                {
                    if (!running)
                    {
                        LOG_VERBOSE("ending start_loop", {});
                        return;
                    }
                    condition_tasks.wait(lock, [&] { return (!queue_tasks.empty() || !running); });
                }
            }
        }
    }

    //
    // functions to manage multitasks
    //

    // add a multitask by specifying the id of all subtask (subtask is a server_task)
    void add_multitask(int id_multi, std::vector<int> &sub_ids)
    {
        std::lock_guard<std::mutex> lock(mutex_tasks);
        server_task_multi multi;
        multi.id = id_multi;
        std::copy(sub_ids.begin(), sub_ids.end(),
                  std::inserter(multi.subtasks_remaining, multi.subtasks_remaining.end()));
        queue_multitasks.push_back(multi);
    }

    // updatethe remaining subtasks, while appending results to multitask
    void update_multitask(int id_multi, int id_sub, server_task_result &result)
    {
        std::lock_guard<std::mutex> lock(mutex_tasks);
        for (auto &multitask : queue_multitasks)
        {
            if (multitask.id == id_multi)
            {
                multitask.subtasks_remaining.erase(id_sub);
                multitask.results.push_back(result);
            }
        }
    }
};

struct server_response
{
    typedef std::function<void(int, int, server_task_result &)> callback_multitask_t;
    callback_multitask_t callback_update_multitask;

    // for keeping track of all tasks waiting for the result
    std::set<int> waiting_task_ids;

    // the main result queue
    std::vector<server_task_result> queue_results;

    std::mutex mutex_results;
    std::condition_variable condition_results;

    // add the id_task to the list of tasks waiting for response
    void add_waiting_task_id(int id_task)
    {
        LOG_VERBOSE("waiting for task id", {{"id_task", id_task}});

        std::unique_lock<std::mutex> lock(mutex_results);
        waiting_task_ids.insert(id_task);
    }

    // when the request is finished, we can remove task associated with it
    void remove_waiting_task_id(int id_task)
    {
        LOG_VERBOSE("remove waiting for task id", {{"id_task", id_task}});

        std::unique_lock<std::mutex> lock(mutex_results);
        waiting_task_ids.erase(id_task);
    }

    // This function blocks the thread until there is a response for this id_task
    server_task_result recv(int id_task)
    {
        while (true)
        {
            std::unique_lock<std::mutex> lock(mutex_results);
            condition_results.wait(lock, [&] { return !queue_results.empty(); });

            for (int i = 0; i < (int)queue_results.size(); i++)
            {
                if (queue_results[i].id == id_task)
                {
                    assert(queue_results[i].id_multi == -1);
                    server_task_result res = queue_results[i];
                    queue_results.erase(queue_results.begin() + i);
                    return res;
                }
            }
        }

        // should never reach here
    }

    // Register the function to update multitask
    void on_multitask_update(callback_multitask_t callback)
    {
        callback_update_multitask = std::move(callback);
    }

    // Send a new result to a waiting id_task
    void send(server_task_result result)
    {
        LOG_VERBOSE("send new result", {{"id_task", result.id}});

        std::unique_lock<std::mutex> lock(mutex_results);
        for (const auto &id_task : waiting_task_ids)
        {
            // LOG_TEE("waiting task id %i \n", id_task);
            // for now, tasks that have associated parent multitasks just get erased once multitask picks up the result
            if (result.id_multi == id_task)
            {
                LOG_VERBOSE("callback_update_multitask", {{"id_task", id_task}});
                callback_update_multitask(id_task, result.id, result);
                continue;
            }

            if (result.id == id_task)
            {
                LOG_VERBOSE("queue_results.push_back", {{"id_task", id_task}});
                queue_results.push_back(result);
                condition_results.notify_all();
                return;
            }
        }
    }
};

struct server_context
{
    llama_model *model = nullptr;
    llama_context *ctx = nullptr;

    gpt_params params;

    llama_batch batch;

    bool clean_kv_cache = true;
    bool add_bos_token = true;

    int32_t n_ctx; // total context for all clients / slots

    // system prompt
    bool system_need_update = false;

    std::string system_prompt;
    std::vector<llama_token> system_tokens;

    // slots / clients
    std::vector<server_slot> slots;
    json default_generation_settings_for_props;

    server_queue queue_tasks;
    server_response queue_results;

    server_metrics metrics;

    // Necessary similarity of prompt for slot selection
    float slot_prompt_similarity = 0.0f;

    ~server_context()
    {
        if (ctx)
        {
            llama_free(ctx);
            ctx = nullptr;
        }

        if (model)
        {
            llama_free_model(model);
            model = nullptr;
        }

        // Clear any sampling context
        for (server_slot &slot : slots)
        {
            if (slot.ctx_sampling != nullptr)
            {
                llama_sampling_free(slot.ctx_sampling);
            }
        }

        llama_batch_free(batch);
    }

    bool load_model(const gpt_params &params_)
    {
        params = params_;

        // dedicate one sequence to the system prompt
        params.n_parallel += 1;

        llama_init_result llama_init = llama_init_from_gpt_params(params);

        model = llama_init.model;
        ctx = llama_init.context;
        params.n_parallel -= 1; // but be sneaky about it
        if (model == nullptr)
        {
            LOG_ERROR("unable to load model", {{"model", params.model}});
            return false;
        }

        n_ctx = llama_n_ctx(ctx);

        add_bos_token = llama_should_add_bos_token(model);
        GGML_ASSERT(llama_add_eos_token(model) != 1);

        return true;
    }

    bool validate_model_chat_template() const
    {
        llama_chat_message chat[] = {{"user", "test"}};

        const int res = llama_chat_apply_template(model, nullptr, chat, 1, true, nullptr, 0);

        return res > 0;
    }

    void init()
    {
        const int32_t n_ctx_slot = n_ctx / params.n_parallel;

        LOG_INFO("initializing slots", {{"n_slots", params.n_parallel}});

        for (int i = 0; i < params.n_parallel; i++)
        {
            server_slot slot;

            slot.id = i;
            slot.n_ctx = n_ctx_slot;
            slot.n_predict = params.n_predict;

            LOG_INFO("new slot", {{"id_slot", slot.id}, {"n_ctx_slot", slot.n_ctx}});

            const int ga_n = params.grp_attn_n;
            const int ga_w = params.grp_attn_w;

            if (ga_n != 1)
            {
                GGML_ASSERT(ga_n > 0 && "ga_n must be positive");                   // NOLINT
                GGML_ASSERT(ga_w % ga_n == 0 && "ga_w must be a multiple of ga_n"); // NOLINT
                // GGML_ASSERT(n_ctx_train % ga_w == 0     && "n_ctx_train must be a multiple of ga_w");    // NOLINT
                // GGML_ASSERT(n_ctx >= n_ctx_train * ga_n && "n_ctx must be at least n_ctx_train * ga_n"); // NOLINT

                LOG_INFO("slot self-extend", {{"id_slot", slot.id}, {"ga_n", ga_n}, {"ga_w", ga_w}});
            }

            slot.ga_i = 0;
            slot.ga_n = ga_n;
            slot.ga_w = ga_w;

            slot.sparams = params.sparams;

            slot.reset();

            slots.push_back(slot);
        }

        default_generation_settings_for_props = get_formated_generation(slots.front());
        default_generation_settings_for_props["seed"] = -1;

        // the update_slots() logic will always submit a maximum of n_batch tokens
        // note that n_batch can be > n_ctx (e.g. for non-causal attention models such as BERT where the KV cache is not
        // used)
        {
            const int32_t n_batch = llama_n_batch(ctx);

            // only a single seq_id per token is needed
            batch = llama_batch_init(n_batch, 0, 1);
        }

        metrics.init();
    }

    std::vector<llama_token> tokenize(const json &json_prompt, bool add_special) const
    {
        // TODO: currently, we tokenize using special tokens by default
        //       this is not always correct (see
        //       https://github.com/ggerganov/llama.cpp/pull/4160#issuecomment-1824826216) but it's better compared to
        //       completely ignoring ChatML and other chat templates
        const bool TMP_FORCE_SPECIAL = true;

        // If `add_bos` is true, we only add BOS, when json_prompt is a string,
        // or the first element of the json_prompt array is a string.
        std::vector<llama_token> prompt_tokens;

        if (json_prompt.is_array())
        {
            bool first = true;
            for (const auto &p : json_prompt)
            {
                if (p.is_string())
                {
                    auto s = p.template get<std::string>();

                    std::vector<llama_token> p;
                    if (first)
                    {
                        p = ::llama_tokenize(ctx, s, add_special, TMP_FORCE_SPECIAL);
                        first = false;
                    }
                    else
                    {
                        p = ::llama_tokenize(ctx, s, false, TMP_FORCE_SPECIAL);
                    }

                    prompt_tokens.insert(prompt_tokens.end(), p.begin(), p.end());
                }
                else
                {
                    if (first)
                    {
                        first = false;
                    }

                    prompt_tokens.push_back(p.template get<llama_token>());
                }
            }
        }
        else
        {
            auto s = json_prompt.template get<std::string>();
            prompt_tokens = ::llama_tokenize(ctx, s, add_special, TMP_FORCE_SPECIAL);
        }

        return prompt_tokens;
    }

    server_slot *get_slot_by_id(int id)
    {
        for (server_slot &slot : slots)
        {
            if (slot.id == id)
            {
                return &slot;
            }
        }

        return nullptr;
    }

    server_slot *get_available_slot(const std::string &prompt)
    {
        server_slot *ret = nullptr;

        // find the slot that has at least n% prompt similarity
        if (ret == nullptr && slot_prompt_similarity != 0.0f && !prompt.empty())
        {
            int max_lcp_len = 0;
            float similarity = 0;

            for (server_slot &slot : slots)
            {
                // skip the slot if it is not available
                if (!slot.available())
                {
                    continue;
                }

                // skip the slot if it does not contains prompt
                if (!slot.prompt.is_string())
                {
                    continue;
                }

                // current slot's prompt
                std::string slot_prompt = slot.prompt.get<std::string>();

                // length of the current slot's prompt
                int slot_prompt_len = slot_prompt.size();

                // length of the Longest Common Prefix between the current slot's prompt and the input prompt
                int lcp_len = common_part(slot_prompt, prompt);

                // fraction of the common substring length compared to the current slot's prompt length
                similarity = static_cast<float>(lcp_len) / slot_prompt_len;

                // select the current slot if the criteria match
                if (lcp_len > max_lcp_len && similarity > slot_prompt_similarity)
                {
                    max_lcp_len = lcp_len;
                    ret = &slot;
                }
            }

            if (ret != nullptr)
            {
                LOG_VERBOSE("selected slot by lcp similarity", {
                                                                   {"id_slot", ret->id},
                                                                   {"max_lcp_len", max_lcp_len},
                                                                   {"similarity", similarity},
                                                               });
            }
        }

        // find the slot that has been least recently used
        if (ret == nullptr)
        {
            int64_t t_last = ggml_time_us();
            for (server_slot &slot : slots)
            {
                // skip the slot if it is not available
                if (!slot.available())
                {
                    continue;
                }

                // select the current slot if the criteria match
                if (slot.t_last_used < t_last)
                {
                    t_last = slot.t_last_used;
                    ret = &slot;
                }
            }

            if (ret != nullptr)
            {
                LOG_VERBOSE("selected slot by lru", {
                                                        {"id_slot", ret->id},
                                                        {"t_last", t_last},
                                                    });
            }
        }

        return ret;
    }

    bool launch_slot_with_task(server_slot &slot, const server_task &task)
    {
        slot_params default_params;
        // Sampling parameter defaults are loaded from the global server context (but individual requests can still
        // override them)
        llama_sampling_params default_sparams = params.sparams;
        auto &data = task.data;

        slot.oaicompat = false;
        slot.oaicompat_model = "";

        slot.params.stream = json_value(data, "stream", false);
        slot.params.cache_prompt = json_value(data, "cache_prompt", false);
        slot.params.n_predict = json_value(data, "n_predict", json_value(data, "max_tokens", default_params.n_predict));
        slot.sparams.top_k = json_value(data, "top_k", default_sparams.top_k);
        slot.sparams.top_p = json_value(data, "top_p", default_sparams.top_p);
        slot.sparams.min_p = json_value(data, "min_p", default_sparams.min_p);
        slot.sparams.tfs_z = json_value(data, "tfs_z", default_sparams.tfs_z);
        slot.sparams.typical_p = json_value(data, "typical_p", default_sparams.typical_p);
        slot.sparams.temp = json_value(data, "temperature", default_sparams.temp);
        slot.sparams.dynatemp_range = json_value(data, "dynatemp_range", default_sparams.dynatemp_range);
        slot.sparams.dynatemp_exponent = json_value(data, "dynatemp_exponent", default_sparams.dynatemp_exponent);
        slot.sparams.penalty_last_n = json_value(data, "repeat_last_n", default_sparams.penalty_last_n);
        slot.sparams.penalty_repeat = json_value(data, "repeat_penalty", default_sparams.penalty_repeat);
        slot.sparams.penalty_freq = json_value(data, "frequency_penalty", default_sparams.penalty_freq);
        slot.sparams.penalty_present = json_value(data, "presence_penalty", default_sparams.penalty_present);
        slot.sparams.mirostat = json_value(data, "mirostat", default_sparams.mirostat);
        slot.sparams.mirostat_tau = json_value(data, "mirostat_tau", default_sparams.mirostat_tau);
        slot.sparams.mirostat_eta = json_value(data, "mirostat_eta", default_sparams.mirostat_eta);
        slot.sparams.penalize_nl = json_value(data, "penalize_nl", default_sparams.penalize_nl);
        slot.params.n_keep = json_value(data, "n_keep", slot.params.n_keep);
        slot.params.n_discard = json_value(data, "n_discard", default_params.n_discard);
        slot.sparams.seed = json_value(data, "seed", default_sparams.seed);
        slot.sparams.n_probs = json_value(data, "n_probs", default_sparams.n_probs);
        slot.sparams.min_keep = json_value(data, "min_keep", default_sparams.min_keep);
        slot.sparams.grammar = json_value(data, "grammar", default_sparams.grammar);

        if (slot.params.cache_prompt && slot.ga_n != 1)
        {
            LOG_WARNING("cache_prompt is not supported with group-attention", {});
            slot.params.cache_prompt = false;
        }

        if (slot.n_predict > 0 && slot.params.n_predict > slot.n_predict)
        {
            // Might be better to reject the request with a 400 ?
            LOG_WARNING("Max tokens to predict exceeds server configuration",
                        {
                            {"params.n_predict", slot.params.n_predict},
                            {"slot.n_predict", slot.n_predict},
                        });
            slot.params.n_predict = slot.n_predict;
        }

        // infill
        slot.params.input_prefix = json_value(data, "input_prefix", default_params.input_prefix);
        slot.params.input_suffix = json_value(data, "input_suffix", default_params.input_suffix);

        // get prompt
        if (!task.infill)
        {
            const auto &prompt = data.find("prompt");
            if (prompt == data.end())
            {
                send_error(task, "\"prompt\" must be provided", ERROR_TYPE_INVALID_REQUEST);
                return false;
            }

            if ((prompt->is_string()) || (prompt->is_array() && prompt->size() == 1 && prompt->at(0).is_string()) ||
                (prompt->is_array() && !prompt->empty() && prompt->at(0).is_number_integer()))
            {
                slot.prompt = *prompt;
            }
            else
            {
                send_error(task, "\"prompt\" must be a string or an array of integers", ERROR_TYPE_INVALID_REQUEST);
                return false;
            }
        }

        // penalize user-provided tokens
        {
            slot.sparams.penalty_prompt_tokens.clear();
            slot.sparams.use_penalty_prompt_tokens = false;

            const auto &penalty_prompt = data.find("penalty_prompt");

            if (penalty_prompt != data.end())
            {
                if (penalty_prompt->is_string())
                {
                    const auto penalty_prompt_string = penalty_prompt->get<std::string>();
                    slot.sparams.penalty_prompt_tokens = llama_tokenize(model, penalty_prompt_string, false);

                    if (slot.params.n_predict > 0)
                    {
                        slot.sparams.penalty_prompt_tokens.reserve(slot.sparams.penalty_prompt_tokens.size() +
                                                                   slot.params.n_predict);
                    }
                    slot.sparams.use_penalty_prompt_tokens = true;

                    LOG_VERBOSE("penalty_prompt_tokens", {
                                                             {"id_slot", slot.id},
                                                             {"tokens", slot.sparams.penalty_prompt_tokens},
                                                         });
                }
                else if (penalty_prompt->is_array())
                {
                    const auto n_tokens = penalty_prompt->size();
                    slot.sparams.penalty_prompt_tokens.reserve(n_tokens + std::max(0, slot.params.n_predict));

                    const int n_vocab = llama_n_vocab(model);
                    for (const auto &penalty_token : *penalty_prompt)
                    {
                        if (penalty_token.is_number_integer())
                        {
                            const auto tok = penalty_token.get<llama_token>();
                            if (tok >= 0 && tok < n_vocab)
                            {
                                slot.sparams.penalty_prompt_tokens.push_back(tok);
                            }
                        }
                    }
                    slot.sparams.use_penalty_prompt_tokens = true;

                    LOG_VERBOSE("penalty_prompt_tokens", {
                                                             {"id_slot", slot.id},
                                                             {"tokens", slot.sparams.penalty_prompt_tokens},
                                                         });
                }
            }
        }

        {
            slot.sparams.logit_bias.clear();

            if (json_value(data, "ignore_eos", false))
            {
                slot.sparams.logit_bias[llama_token_eos(model)] = -INFINITY;
            }

            const auto &logit_bias = data.find("logit_bias");
            if (logit_bias != data.end() && logit_bias->is_array())
            {
                const int n_vocab = llama_n_vocab(model);
                for (const auto &el : *logit_bias)
                {
                    // TODO: we may want to throw errors here, in case "el" is incorrect
                    if (el.is_array() && el.size() == 2)
                    {
                        float bias;
                        if (el[1].is_number())
                        {
                            bias = el[1].get<float>();
                        }
                        else if (el[1].is_boolean() && !el[1].get<bool>())
                        {
                            bias = -INFINITY;
                        }
                        else
                        {
                            continue;
                        }

                        if (el[0].is_number_integer())
                        {
                            llama_token tok = el[0].get<llama_token>();
                            if (tok >= 0 && tok < n_vocab)
                            {
                                slot.sparams.logit_bias[tok] = bias;
                            }
                        }
                        else if (el[0].is_string())
                        {
                            auto toks = llama_tokenize(model, el[0].get<std::string>(), false);
                            for (auto tok : toks)
                            {
                                slot.sparams.logit_bias[tok] = bias;
                            }
                        }
                    }
                }
            }
        }

        {
            slot.params.antiprompt.clear();

            const auto &stop = data.find("stop");
            if (stop != data.end() && stop->is_array())
            {
                for (const auto &word : *stop)
                {
                    if (!word.empty())
                    {
                        slot.params.antiprompt.push_back(word);
                    }
                }
            }
        }

        {
            const auto &samplers_sequence = data.find("samplers");
            if (samplers_sequence != data.end() && samplers_sequence->is_array())
            {
                std::vector<std::string> sampler_names;
                for (const auto &sampler_name : *samplers_sequence)
                {
                    if (sampler_name.is_string())
                    {
                        sampler_names.emplace_back(sampler_name);
                    }
                }
                slot.sparams.samplers_sequence = llama_sampling_types_from_names(sampler_names, false);
            }
            else
            {
                slot.sparams.samplers_sequence = default_sparams.samplers_sequence;
            }
        }

        {
            if (slot.ctx_sampling != nullptr)
            {
                llama_sampling_free(slot.ctx_sampling);
            }
            slot.ctx_sampling = llama_sampling_init(slot.sparams);
            if (slot.ctx_sampling == nullptr)
            {
                // for now, the only error that may happen here is invalid grammar
                send_error(task, "Failed to parse grammar", ERROR_TYPE_INVALID_REQUEST);
                return false;
            }
        }

        slot.command = SLOT_COMMAND_LOAD_PROMPT;
        slot.prompt_tokens.clear();

        LOG_INFO("slot is processing task", {
                                                {"id_slot", slot.id},
                                                {"id_task", slot.id_task},
                                            });

        return true;
    }

    void kv_cache_clear()
    {
        LOG_VERBOSE("clearing KV cache", {});

        // clear the entire KV cache
        llama_kv_cache_clear(ctx);
        clean_kv_cache = false;
    }

    void system_prompt_update()
    {
        LOG_VERBOSE("system prompt update", {
                                                {"system_prompt", system_prompt},
                                            });

        kv_cache_clear();
        system_tokens.clear();

        if (!system_prompt.empty())
        {
            system_tokens = ::llama_tokenize(ctx, system_prompt, true);

            llama_batch_clear(batch);

            for (int i = 0; i < (int)system_tokens.size(); ++i)
            {
                llama_batch_add(batch, system_tokens[i], i, {0}, false);
            }

            const int32_t n_batch = llama_n_batch(ctx);

            for (int32_t i = 0; i < batch.n_tokens; i += n_batch)
            {
                const int32_t n_tokens = std::min(params.n_batch, batch.n_tokens - i);
                llama_batch batch_view = {
                    n_tokens,
                    batch.token + i,
                    nullptr,
                    batch.pos + i,
                    batch.n_seq_id + i,
                    batch.seq_id + i,
                    batch.logits + i,
                    0,
                    0,
                    0, // unused
                };

                if (llama_decode(ctx, batch_view) != 0)
                {
                    LOG_ERROR("llama_decode() failed", {});
                    return;
                }
            }

            // assign the system KV cache to all parallel sequences
            for (int32_t i = 1; i <= params.n_parallel; ++i)
            {
                llama_kv_cache_seq_cp(ctx, 0, i, -1, -1);
            }
        }

        system_need_update = false;
    }

    bool system_prompt_set(const std::string &sys_prompt)
    {
        system_prompt = sys_prompt;

        LOG_VERBOSE("system prompt process", {
                                                 {"system_prompt", system_prompt},
                                             });

        // release all slots
        for (server_slot &slot : slots)
        {
            slot.release();
        }

        system_need_update = true;
        return true;
    }

    bool process_token(completion_token_output &result, server_slot &slot)
    {
        // remember which tokens were sampled - used for repetition penalties during sampling
        const std::string token_str = llama_token_to_piece(ctx, result.tok, params.special);
        slot.sampled = result.tok;

        // search stop word and delete it
        slot.generated_text += token_str;
        slot.has_next_token = true;

        if (slot.ctx_sampling->params.use_penalty_prompt_tokens && result.tok != -1)
        {
            // we can change penalty_prompt_tokens because it is always created from scratch each request
            slot.ctx_sampling->params.penalty_prompt_tokens.push_back(result.tok);
        }

        // check if there is incomplete UTF-8 character at the end
        bool incomplete = false;
        for (unsigned i = 1; i < 5 && i <= slot.generated_text.size(); ++i)
        {
            unsigned char c = slot.generated_text[slot.generated_text.size() - i];
            if ((c & 0xC0) == 0x80)
            {
                // continuation byte: 10xxxxxx
                continue;
            }
            if ((c & 0xE0) == 0xC0)
            {
                // 2-byte character: 110xxxxx ...
                incomplete = i < 2;
            }
            else if ((c & 0xF0) == 0xE0)
            {
                // 3-byte character: 1110xxxx ...
                incomplete = i < 3;
            }
            else if ((c & 0xF8) == 0xF0)
            {
                // 4-byte character: 11110xxx ...
                incomplete = i < 4;
            }
            // else 1-byte character or invalid byte
            break;
        }

        if (!incomplete)
        {
            size_t pos = std::min(slot.n_sent_text, slot.generated_text.size());

            const std::string str_test = slot.generated_text.substr(pos);
            bool is_stop_full = false;

            size_t stop_pos = slot.find_stopping_strings(str_test, token_str.size(), STOP_TYPE_FULL);
            if (stop_pos != std::string::npos)
            {
                is_stop_full = true;
                slot.generated_text.erase(slot.generated_text.begin() + pos + stop_pos, slot.generated_text.end());
                pos = std::min(slot.n_sent_text, slot.generated_text.size());
            }
            else
            {
                is_stop_full = false;
                stop_pos = slot.find_stopping_strings(str_test, token_str.size(), STOP_TYPE_PARTIAL);
            }

            // check if there is any token to predict
            if (stop_pos == std::string::npos || (!slot.has_next_token && !is_stop_full && stop_pos > 0))
            {
                // no send the stop word in the response
                result.text_to_send = slot.generated_text.substr(pos, std::string::npos);
                slot.n_sent_text += result.text_to_send.size();
                // add the token to slot queue and cache
            }

            slot.add_token_string(result);
            if (slot.params.stream)
            {
                send_partial_response(slot, result);
            }
        }

        if (incomplete)
        {
            slot.has_next_token = true;
        }

        // check the limits
        if (slot.n_decoded > 0 && slot.has_next_token && !slot.has_budget(params))
        {
            slot.stopped_limit = true;
            slot.has_next_token = false;

            LOG_VERBOSE("stopped by limit", {
                                                {"id_slot", slot.id},
                                                {"id_task", slot.id_task},
                                                {"n_decoded", slot.n_decoded},
                                                {"n_predict", slot.params.n_predict},
                                            });
        }

        if (llama_token_is_eog(model, result.tok))
        {
            slot.stopped_eos = true;
            slot.has_next_token = false;

            LOG_VERBOSE("eos token found", {});
        }

        auto n_ctx_train = llama_n_ctx_train(model);
        if (slot.params.n_predict < 1 && slot.n_predict < 1 && slot.ga_n == 1 &&
            slot.n_prompt_tokens + slot.n_decoded >= n_ctx_train)
        {
            LOG_WARNING("n_predict is not set and self-context extend is disabled."
                        " Limiting generated tokens to n_ctx_train to avoid EOS-less generation infinite loop",
                        {
                            {"id_slot", slot.id},
                            {"params.n_predict", slot.params.n_predict},
                            {"slot.n_prompt_tokens", slot.n_prompt_tokens},
                            {"slot.n_decoded", slot.n_decoded},
                            {"slot.n_predict", slot.n_predict},
                            {"n_slots", params.n_parallel},
                            {"slot.n_ctx", slot.n_ctx},
                            {"n_ctx", n_ctx},
                            {"n_ctx_train", n_ctx_train},
                            {"ga_n", slot.ga_n},
                        });
            slot.truncated = true;
            slot.stopped_limit = true;
            slot.has_next_token = false; // stop prediction
        }

        LOG_VERBOSE("next token", {
                                      {"id_slot", slot.id},
                                      {"id_task", slot.id_task},
                                      {"token", result.tok},
                                      {"token_text", tokens_to_output_formatted_string(ctx, result.tok)},
                                      {"has_next_token", slot.has_next_token},
                                      {"n_remain", slot.n_remaining},
                                      {"n_decoded", slot.n_decoded},
                                      {"stopped_eos", slot.stopped_eos},
                                      {"stopped_word", slot.stopped_word},
                                      {"stopped_limit", slot.stopped_limit},
                                      {"stopping_word", slot.stopping_word},
                                  });

        return slot.has_next_token; // continue
    }

    json get_formated_generation(const server_slot &slot) const
    {
        const auto eos_bias = slot.sparams.logit_bias.find(llama_token_eos(model));
        const bool ignore_eos =
            eos_bias != slot.sparams.logit_bias.end() && eos_bias->second < 0.0f && std::isinf(eos_bias->second);

        std::vector<std::string> samplers_sequence;
        samplers_sequence.reserve(slot.sparams.samplers_sequence.size());
        for (const auto &sampler_type : slot.sparams.samplers_sequence)
        {
            samplers_sequence.emplace_back(llama_sampling_type_to_str(sampler_type));
        }

        return json{{"n_ctx", slot.n_ctx},
                    {"n_predict", slot.n_predict},
                    {"model", params.model_alias},
                    {"seed", slot.sparams.seed},
                    {"temperature", slot.sparams.temp},
                    {"dynatemp_range", slot.sparams.dynatemp_range},
                    {"dynatemp_exponent", slot.sparams.dynatemp_exponent},
                    {"top_k", slot.sparams.top_k},
                    {"top_p", slot.sparams.top_p},
                    {"min_p", slot.sparams.min_p},
                    {"tfs_z", slot.sparams.tfs_z},
                    {"typical_p", slot.sparams.typical_p},
                    {"repeat_last_n", slot.sparams.penalty_last_n},
                    {"repeat_penalty", slot.sparams.penalty_repeat},
                    {"presence_penalty", slot.sparams.penalty_present},
                    {"frequency_penalty", slot.sparams.penalty_freq},
                    {"penalty_prompt_tokens", slot.sparams.penalty_prompt_tokens},
                    {"use_penalty_prompt_tokens", slot.sparams.use_penalty_prompt_tokens},
                    {"mirostat", slot.sparams.mirostat},
                    {"mirostat_tau", slot.sparams.mirostat_tau},
                    {"mirostat_eta", slot.sparams.mirostat_eta},
                    {"penalize_nl", slot.sparams.penalize_nl},
                    {"stop", slot.params.antiprompt},
                    {"n_predict", slot.params.n_predict}, // TODO: fix duplicate key n_predict
                    {"n_keep", slot.params.n_keep},
                    {"n_discard", slot.params.n_discard},
                    {"ignore_eos", ignore_eos},
                    {"stream", slot.params.stream},
                    {"logit_bias", slot.sparams.logit_bias},
                    {"n_probs", slot.sparams.n_probs},
                    {"min_keep", slot.sparams.min_keep},
                    {"grammar", slot.sparams.grammar},
                    {"samplers", samplers_sequence}};
    }

    void send_error(const server_task &task, const std::string &error, const enum error_type type = ERROR_TYPE_SERVER)
    {
        send_error(task.id, task.id_multi, error, type);
    }

    void send_error(const server_slot &slot, const std::string &error, const enum error_type type = ERROR_TYPE_SERVER)
    {
        send_error(slot.id_task, slot.id_multi, error, type);
    }

    void send_error(const int id_task, const int id_multi, const std::string &error,
                    const enum error_type type = ERROR_TYPE_SERVER)
    {
        LOG_ERROR("task error", {
                                    {"id_multi", id_multi},
                                    {"id_task", id_task},
                                    {"error", error},
                                });

        server_task_result res;
        res.id = id_task;
        res.id_multi = id_multi;
        res.stop = false;
        res.error = true;
        res.data = format_error_response(error, type);

        queue_results.send(res);
    }

    void send_partial_response(server_slot &slot, completion_token_output tkn)
    {
        server_task_result res;
        res.id = slot.id_task;
        res.id_multi = slot.id_multi;
        res.error = false;
        res.stop = false;
        res.data = json{{"content", tkn.text_to_send}, {"stop", false}, {"id_slot", slot.id}, {"multimodal", false}};

        if (slot.sparams.n_probs > 0)
        {
            const std::vector<llama_token> to_send_toks = llama_tokenize(ctx, tkn.text_to_send, false);
            const size_t probs_pos = std::min(slot.n_sent_token_probs, slot.generated_token_probs.size());
            const size_t probs_stop_pos =
                std::min(slot.n_sent_token_probs + to_send_toks.size(), slot.generated_token_probs.size());

            std::vector<completion_token_output> probs_output;
            if (probs_pos < probs_stop_pos)
            {
                probs_output =
                    std::vector<completion_token_output>(slot.generated_token_probs.begin() + probs_pos,
                                                         slot.generated_token_probs.begin() + probs_stop_pos);
            }
            slot.n_sent_token_probs = probs_stop_pos;

            res.data["completion_probabilities"] = probs_vector_to_json(ctx, probs_output);
        }

        if (slot.oaicompat)
        {
            res.data["oaicompat_token_ctr"] = slot.n_decoded;
            res.data["model"] = slot.oaicompat_model;
        }

        queue_results.send(res);
    }

    void send_final_response(const server_slot &slot)
    {
        server_task_result res;
        res.id = slot.id_task;
        res.id_multi = slot.id_multi;
        res.error = false;
        res.stop = true;
        res.data = json{{"content", !slot.params.stream ? slot.generated_text : ""},
                        {"id_slot", slot.id},
                        {"stop", true},
                        {"model", params.model_alias},
                        {"tokens_predicted", slot.n_decoded},
                        {"tokens_evaluated", slot.n_prompt_tokens},
                        {"generation_settings", get_formated_generation(slot)},
                        {"prompt", slot.prompt},
                        {"truncated", slot.truncated},
                        {"stopped_eos", slot.stopped_eos},
                        {"stopped_word", slot.stopped_word},
                        {"stopped_limit", slot.stopped_limit},
                        {"stopping_word", slot.stopping_word},
                        {"tokens_cached", slot.n_past},
                        {"timings", slot.get_formated_timings()}};

        if (slot.sparams.n_probs > 0)
        {
            std::vector<completion_token_output> probs;
            if (!slot.params.stream && slot.stopped_word)
            {
                const std::vector<llama_token> stop_word_toks = llama_tokenize(ctx, slot.stopping_word, false);

                size_t safe_offset = std::min(slot.generated_token_probs.size(), stop_word_toks.size());
                probs = std::vector<completion_token_output>(slot.generated_token_probs.begin(),
                                                             slot.generated_token_probs.end() - safe_offset);
            }
            else
            {
                probs = std::vector<completion_token_output>(slot.generated_token_probs.begin(),
                                                             slot.generated_token_probs.end());
            }

            res.data["completion_probabilities"] = probs_vector_to_json(ctx, probs);
        }

        if (slot.oaicompat)
        {
            res.data["oaicompat_token_ctr"] = slot.n_decoded;
            res.data["model"] = slot.oaicompat_model;
        }

        queue_results.send(res);
    }

    void send_embedding(const server_slot &slot, const llama_batch &batch)
    {
        server_task_result res;
        res.id = slot.id_task;
        res.id_multi = slot.id_multi;
        res.error = false;
        res.stop = true;

        const int n_embd = llama_n_embd(model);

        std::vector<float> embd_res(n_embd, 0.0f);

        for (int i = 0; i < batch.n_tokens; ++i)
        {
            if (!batch.logits[i] || batch.seq_id[i][0] != slot.id + 1)
            {
                continue;
            }

            const float *embd = llama_get_embeddings_seq(ctx, batch.seq_id[i][0]);
            if (embd == NULL)
            {
                embd = llama_get_embeddings_ith(ctx, i);
            }

            if (embd == NULL)
            {
                LOG_ERROR("failed to get embeddings", {{"token", batch.token[i]}, {"seq_id", batch.seq_id[i][0]}});

                res.data = json{
                    {"embedding", std::vector<float>(n_embd, 0.0f)},
                };

                continue;
            }

            llama_embd_normalize(embd, embd_res.data(), n_embd);

            res.data = json{
                {"embedding", embd_res},
            };
        }

        queue_results.send(res);
    }

    void request_completion(int id_task, int id_multi, json data, bool infill, bool embedding)
    {
        server_task task;
        task.id = id_task;
        task.id_multi = id_multi;
        task.id_target = 0;
        task.data = std::move(data);
        task.infill = infill;
        task.embedding = embedding;
        task.type = SERVER_TASK_TYPE_COMPLETION;

        // when a completion task's prompt array is not a singleton, we split it into multiple requests
        // otherwise, it's a single-prompt task, we actually queue it
        // if there's numbers in the prompt array it will be treated as an array of tokens
        if (task.data.count("prompt") != 0 && task.data.at("prompt").size() > 1)
        {
            bool numbers = false;
            for (const auto &e : task.data.at("prompt"))
            {
                if (e.is_number())
                {
                    numbers = true;
                    break;
                }
            }

            // NOTE: split_multiprompt_task() does not handle a mix of strings and numbers,
            // it will completely stall the server. I don't know where the bug for this is.
            //
            // if there are numbers, it needs to be treated like a single prompt,
            // queue_tasks handles a mix of strings and numbers just fine.
            if (numbers)
            {
                queue_tasks.post(task);
            }
            else
            {
                split_multiprompt_task(id_task, task);
            }
        }
        else
        {
            queue_tasks.post(task);
        }
    }

    void request_cancel(int id_task)
    {
        server_task task;
        task.type = SERVER_TASK_TYPE_CANCEL;
        task.id_target = id_task;

        queue_tasks.post(task);
    }

    void split_multiprompt_task(int id_multi, const server_task &multiprompt_task)
    {
        const int prompt_count = multiprompt_task.data.at("prompt").size();
        if (prompt_count <= 1)
        {
            send_error(multiprompt_task, "error while handling multiple prompts");
            return;
        }

        // generate all the ID for subtask
        std::vector<int> subtask_ids(prompt_count);
        for (int i = 0; i < prompt_count; i++)
        {
            subtask_ids[i] = queue_tasks.get_new_id();
        }

        // queue up the multitask so we can track its subtask progression
        queue_tasks.add_multitask(id_multi, subtask_ids);

        // add subtasks
        for (int i = 0; i < prompt_count; i++)
        {
            json subtask_data = multiprompt_task.data;
            subtask_data["prompt"] = subtask_data.at("prompt")[i];

            // subtasks inherit everything else (infill mode, embedding mode, etc.)
            request_completion(subtask_ids[i], id_multi, subtask_data, multiprompt_task.infill,
                               multiprompt_task.embedding);
        }
    }

    void process_single_task(const server_task &task)
    {
        switch (task.type)
        {
        case SERVER_TASK_TYPE_COMPLETION: {
            const int id_slot = json_value(task.data, "id_slot", -1);

            server_slot *slot;

            if (id_slot != -1)
            {
                slot = get_slot_by_id(id_slot);
            }
            else
            {
                std::string prompt;
                if (task.data.contains("prompt") && task.data.at("prompt").is_string())
                {
                    prompt = json_value(task.data, "prompt", std::string());
                }

                slot = get_available_slot(prompt);
            }

            if (slot == nullptr)
            {
                // if no slot is available, we defer this task for processing later
                LOG_VERBOSE("no slot is available", {{"id_task", task.id}});
                queue_tasks.defer(task);
                break;
            }
            if (!slot->available())
            {
                // if requested slot is unavailable, we defer this task for processing later
                LOG_VERBOSE("requested slot is unavailable", {{"id_task", task.id}});
                queue_tasks.defer(task);
                break;
            }

            if (task.data.contains("system_prompt"))
            {
                std::string sys_prompt = json_value(task.data, "system_prompt", std::string());
                system_prompt_set(sys_prompt);

                for (server_slot &slot : slots)
                {
                    slot.n_past = 0;
                    slot.n_past_se = 0;
                }
            }

            slot->reset();

            slot->id_task = task.id;
            slot->id_multi = task.id_multi;
            slot->infill = task.infill;
            slot->embedding = task.embedding;

            if (!launch_slot_with_task(*slot, task))
            {
                LOG_ERROR("error while launching slot", task.data);
                break;
            }
        }
        break;
        case SERVER_TASK_TYPE_CANCEL: {
            // release slot linked with the task id
            for (auto &slot : slots)
            {
                if (slot.id_task == task.id_target)
                {
                    slot.release();
                    break;
                }
            }
        }
        break;
        case SERVER_TASK_TYPE_NEXT_RESPONSE: {
            // do nothing
        }
        break;
        case SERVER_TASK_TYPE_METRICS: {
            json slots_data = json::array();

            int n_idle_slots = 0;
            int n_processing_slots = 0;

            for (server_slot &slot : slots)
            {
                json slot_data = get_formated_generation(slot);
                slot_data["id"] = slot.id;
                slot_data["id_task"] = slot.id_task;
                slot_data["state"] = slot.state;
                slot_data["prompt"] = slot.prompt;
                slot_data["next_token"] = {
                    {"has_next_token", slot.has_next_token}, {"n_remain", slot.n_remaining},
                    {"n_decoded", slot.n_decoded},           {"stopped_eos", slot.stopped_eos},
                    {"stopped_word", slot.stopped_word},     {"stopped_limit", slot.stopped_limit},
                    {"stopping_word", slot.stopping_word},
                };

                if (slot_data["state"] == SLOT_STATE_IDLE)
                {
                    n_idle_slots++;
                }
                else
                {
                    n_processing_slots++;
                }

                slots_data.push_back(slot_data);
            }
            LOG_INFO(
                "slot data",
                {{"id_task", task.id}, {"n_idle_slots", n_idle_slots}, {"n_processing_slots", n_processing_slots}});

            LOG_VERBOSE("slot data", {{"id_task", task.id},
                                      {"n_idle_slots", n_idle_slots},
                                      {"n_processing_slots", n_processing_slots},
                                      {"slots", slots_data}});

            server_task_result res;
            res.id = task.id;
            res.id_multi = task.id_multi;
            res.stop = true;
            res.error = false;
            res.data = {
                {"idle", n_idle_slots},
                {"processing", n_processing_slots},
                {"deferred", queue_tasks.queue_tasks_deferred.size()},
                {"t_start", metrics.t_start},

                {"n_prompt_tokens_processed_total", metrics.n_prompt_tokens_processed_total},
                {"t_tokens_generation_total", metrics.t_tokens_generation_total},
                {"n_tokens_predicted_total", metrics.n_tokens_predicted_total},
                {"t_prompt_processing_total", metrics.t_prompt_processing_total},

                {"n_prompt_tokens_processed", metrics.n_prompt_tokens_processed},
                {"t_prompt_processing", metrics.t_prompt_processing},
                {"n_tokens_predicted", metrics.n_tokens_predicted},
                {"t_tokens_generation", metrics.t_tokens_generation},

                {"kv_cache_tokens_count", llama_get_kv_cache_token_count(ctx)},
                {"kv_cache_used_cells", llama_get_kv_cache_used_cells(ctx)},

                {"slots", slots_data},
            };

            if (json_value(task.data, "reset_bucket", false))
            {
                metrics.reset_bucket();
            }
            queue_results.send(res);
        }
        break;
        case SERVER_TASK_TYPE_SLOT_SAVE: {
            int id_slot = task.data.at("id_slot");
            server_slot *slot = get_slot_by_id(id_slot);
            if (slot == nullptr)
            {
                send_error(task, "Invalid slot ID", ERROR_TYPE_INVALID_REQUEST);
                break;
            }
            if (!slot->available())
            {
                // if requested slot is unavailable, we defer this task for processing later
                LOG_VERBOSE("requested slot is unavailable", {{"id_task", task.id}});
                queue_tasks.defer(task);
                break;
            }

            const size_t token_count = slot->cache_tokens.size();
            const int64_t t_start = ggml_time_us();

            std::string filename = task.data.at("filename");
            std::string filepath = task.data.at("filepath");

            const size_t nwrite =
                llama_state_seq_save_file(ctx, filepath.c_str(), slot->id + 1, slot->cache_tokens.data(), token_count);

            const int64_t t_end = ggml_time_us();
            const double t_save_ms = (t_end - t_start) / 1000.0;

            server_task_result result;
            result.id = task.id;
            result.stop = true;
            result.error = false;
            result.data = json{{"id_slot", id_slot},
                               {"filename", filename},
                               {"n_saved", token_count}, // tokens saved
                               {"n_written", nwrite},    // bytes written
                               {"timings", {{"save_ms", t_save_ms}}}};
            queue_results.send(result);
        }
        break;
        case SERVER_TASK_TYPE_SLOT_RESTORE: {
            int id_slot = task.data.at("id_slot");
            server_slot *slot = get_slot_by_id(id_slot);
            if (slot == nullptr)
            {
                send_error(task, "Invalid slot ID", ERROR_TYPE_INVALID_REQUEST);
                break;
            }
            if (!slot->available())
            {
                // if requested slot is unavailable, we defer this task for processing later
                LOG_VERBOSE("requested slot is unavailable", {{"id_task", task.id}});
                queue_tasks.defer(task);
                break;
            }

            const int64_t t_start = ggml_time_us();

            std::string filename = task.data.at("filename");
            std::string filepath = task.data.at("filepath");

            slot->cache_tokens.resize(slot->n_ctx);
            size_t token_count = 0;
            size_t nread = llama_state_seq_load_file(ctx, filepath.c_str(), slot->id + 1, slot->cache_tokens.data(),
                                                     slot->cache_tokens.size(), &token_count);
            if (nread == 0)
            {
                slot->cache_tokens.resize(0);
                send_error(task, "Unable to restore slot, no available space in KV cache or invalid slot save file",
                           ERROR_TYPE_INVALID_REQUEST);
                break;
            }
            slot->cache_tokens.resize(token_count);

            const int64_t t_end = ggml_time_us();
            const double t_restore_ms = (t_end - t_start) / 1000.0;

            server_task_result result;
            result.id = task.id;
            result.stop = true;
            result.error = false;
            result.data = json{{"id_slot", id_slot},
                               {"filename", filename},
                               {"n_restored", token_count}, // tokens restored
                               {"n_read", nread},           // bytes read
                               {"timings", {{"restore_ms", t_restore_ms}}}};
            queue_results.send(result);
        }
        break;
        case SERVER_TASK_TYPE_SLOT_ERASE: {
            int id_slot = task.data.at("id_slot");
            server_slot *slot = get_slot_by_id(id_slot);
            if (slot == nullptr)
            {
                send_error(task, "Invalid slot ID", ERROR_TYPE_INVALID_REQUEST);
                break;
            }
            if (!slot->available())
            {
                // if requested slot is unavailable, we defer this task for processing later
                LOG_VERBOSE("requested slot is unavailable", {{"id_task", task.id}});
                queue_tasks.defer(task);
                break;
            }

            // Erase token cache
            const size_t n_erased = slot->cache_tokens.size();
            llama_kv_cache_seq_rm(ctx, slot->id + 1, -1, -1);
            slot->cache_tokens.clear();

            server_task_result result;
            result.id = task.id;
            result.stop = true;
            result.error = false;
            result.data = json{{"id_slot", id_slot}, {"n_erased", n_erased}};
            queue_results.send(result);
        }
        break;
        }
    }

    void on_finish_multitask(const server_task_multi &multitask)
    {
        // all subtasks done == multitask is done
        server_task_result result;
        result.id = multitask.id;
        result.stop = true;
        result.error = false;

        // collect json results into one json result
        std::vector<json> result_jsons;
        for (const auto &subres : multitask.results)
        {
            result_jsons.push_back(subres.data);
            result.error = result.error && subres.error;
        }
        result.data = json{{"results", result_jsons}};

        queue_results.send(result);
    }

    void update_slots()
    {
        if (system_need_update)
        {
            system_prompt_update();
        }

        // release slots
        for (auto &slot : slots)
        {
            if (slot.command == SLOT_COMMAND_RELEASE)
            {
                slot.state = SLOT_STATE_IDLE;
                slot.command = SLOT_COMMAND_NONE;
                slot.t_last_used = ggml_time_us();

                LOG_INFO("slot released", {{"id_slot", slot.id},
                                           {"id_task", slot.id_task},
                                           {"n_ctx", n_ctx},
                                           {"n_past", slot.n_past},
                                           {"n_system_tokens", system_tokens.size()},
                                           {"n_cache_tokens", slot.cache_tokens.size()},
                                           {"truncated", slot.truncated}});

                queue_tasks.notify_slot_changed();
            }
        }

        // check if all slots are idle
        {
            bool all_idle = true;

            for (auto &slot : slots)
            {
                if (slot.state != SLOT_STATE_IDLE || slot.command != SLOT_COMMAND_NONE)
                {
                    all_idle = false;
                    break;
                }
            }

            if (all_idle)
            {
                LOG_INFO("all slots are idle", {});
                if (system_prompt.empty() && clean_kv_cache)
                {
                    kv_cache_clear();
                }

                return;
            }
        }

        {
            LOG_VERBOSE("posting NEXT_RESPONSE", {});

            server_task task;
            task.type = SERVER_TASK_TYPE_NEXT_RESPONSE;
            task.id_target = -1;

            queue_tasks.post(task);
        }

        // apply context-shift if needed
        // TODO: simplify and improve
        for (server_slot &slot : slots)
        {
            if (slot.ga_n == 1)
            {
                if (slot.is_processing() && (int)system_tokens.size() + slot.n_past >= slot.n_ctx - 1)
                {
                    // Shift context
                    const int n_keep = slot.params.n_keep + add_bos_token;
                    const int n_left = (int)system_tokens.size() + slot.n_past - n_keep;
                    const int n_discard = slot.params.n_discard ? slot.params.n_discard : (n_left / 2);

                    LOG_INFO("slot context shift", {{"id_slot", slot.id},
                                                    {"id_task", slot.id_task},
                                                    {"n_keep", n_keep},
                                                    {"n_left", n_left},
                                                    {"n_discard", n_discard},
                                                    {"n_ctx", n_ctx},
                                                    {"n_past", slot.n_past},
                                                    {"n_system_tokens", system_tokens.size()},
                                                    {"n_cache_tokens", slot.cache_tokens.size()}});

                    llama_kv_cache_seq_rm(ctx, slot.id + 1, n_keep, n_keep + n_discard);
                    llama_kv_cache_seq_add(ctx, slot.id + 1, n_keep + n_discard, system_tokens.size() + slot.n_past,
                                           -n_discard);

                    if (slot.params.cache_prompt)
                    {
                        for (size_t i = n_keep + n_discard; i < slot.cache_tokens.size(); i++)
                        {
                            slot.cache_tokens[i - n_discard] = slot.cache_tokens[i];
                        }

                        slot.cache_tokens.resize(slot.cache_tokens.size() - n_discard);
                    }

                    slot.n_past -= n_discard;

                    slot.truncated = true;
                }
            }
        }

        // start populating the batch for this iteration
        llama_batch_clear(batch);

        // frist, add sampled tokens from any ongoing sequences
        for (auto &slot : slots)
        {
            if (slot.state == SLOT_STATE_IDLE)
            {
                continue;
            }

            slot.i_batch = batch.n_tokens;

            const int32_t slot_npast = slot.n_past_se > 0 ? slot.n_past_se : slot.n_past;

            // TODO: we always have to take into account the "system_tokens"
            //       this is not great and needs to be improved somehow
            llama_batch_add(batch, slot.sampled, system_tokens.size() + slot_npast, {slot.id + 1}, true);

            slot.n_past += 1;

            if (slot.params.cache_prompt)
            {
                slot.cache_tokens.push_back(slot.sampled);
            }

            LOG_VERBOSE("slot decode token", {{"id_slot", slot.id},
                                              {"id_task", slot.id_task},
                                              {"n_ctx", n_ctx},
                                              {"n_past", slot.n_past},
                                              {"n_system_tokens", system_tokens.size()},
                                              {"n_cache_tokens", slot.cache_tokens.size()},
                                              {"truncated", slot.truncated}});
        }

        // process in chunks of params.n_batch
        int32_t n_batch = llama_n_batch(ctx);
        int32_t n_ubatch = llama_n_ubatch(ctx);

        // track if this is an embedding or non-embedding batch
        // if we've added sampled tokens above, we are in non-embedding mode
        // -1: none, 0: non-embedding, 1: embedding
        int32_t batch_type = batch.n_tokens > 0 ? 0 : -1;

        // next, batch any pending prompts without exceeding n_batch
        if (params.cont_batching || batch.n_tokens == 0)
        {
            for (auto &slot : slots)
            {
                // this slot still has a prompt to be processed
                if (slot.state == SLOT_STATE_IDLE && slot.command == SLOT_COMMAND_LOAD_PROMPT)
                {
                    auto &prompt_tokens = slot.prompt_tokens;

                    // we haven't tokenized the prompt yet - do it now:
                    if (prompt_tokens.empty())
                    {
                        LOG_VERBOSE("tokenizing prompt", {{"id_slot", slot.id}, {"id_task", slot.id_task}});

                        slot.t_start_process_prompt = ggml_time_us();
                        slot.t_start_generation = 0;

                        if (slot.infill)
                        {
                            const bool add_bos = llama_should_add_bos_token(model);
                            bool suff_rm_leading_spc = true;
                            if (params.input_suffix.find_first_of(' ') == 0 && params.input_suffix.size() > 1)
                            {
                                params.input_suffix.erase(0, 1);
                                suff_rm_leading_spc = false;
                            }

                            auto prefix_tokens = tokenize(slot.params.input_prefix, false);
                            auto suffix_tokens = tokenize(slot.params.input_suffix, false);

                            const int space_token = 29871; // TODO: this should not be hardcoded
                            if (suff_rm_leading_spc && !suffix_tokens.empty() && suffix_tokens[0] == space_token)
                            {
                                suffix_tokens.erase(suffix_tokens.begin());
                            }

                            prefix_tokens.insert(prefix_tokens.begin(), llama_token_prefix(model));
                            suffix_tokens.insert(suffix_tokens.begin(), llama_token_suffix(model));

                            auto embd_inp = params.spm_infill ? suffix_tokens : prefix_tokens;
                            auto embd_end = params.spm_infill ? prefix_tokens : suffix_tokens;
                            if (add_bos)
                            {
                                embd_inp.insert(embd_inp.begin(), llama_token_bos(model));
                            }
                            embd_inp.insert(embd_inp.end(), embd_end.begin(), embd_end.end());

                            const llama_token middle_token = llama_token_middle(model);
                            if (middle_token >= 0)
                            {
                                embd_inp.push_back(middle_token);
                            }

                            prompt_tokens = embd_inp;
                        }
                        else
                        {
                            prompt_tokens =
                                tokenize(slot.prompt, system_prompt.empty()); // add BOS if there isn't system prompt
                        }

                        slot.n_past = 0;
                        slot.n_prompt_tokens = prompt_tokens.size();

                        LOG_VERBOSE("prompt tokenized", {
                                                            {"id_slot", slot.id},
                                                            {"id_task", slot.id_task},
                                                            {"n_ctx", slot.n_ctx},
                                                            {"n_keep", slot.params.n_keep},
                                                            {"n_prompt_tokens", slot.n_prompt_tokens},
                                                            {"prompt_tokens", tokens_to_str(ctx, prompt_tokens.cbegin(),
                                                                                            prompt_tokens.cend())},
                                                        });

                        // empty prompt passed -> release the slot and send empty response
                        if (prompt_tokens.empty())
                        {
                            LOG_INFO("empty prompt - releasing slot",
                                     {{"id_slot", slot.id}, {"id_task", slot.id_task}});

                            slot.state = SLOT_STATE_PROCESSING;
                            slot.command = SLOT_COMMAND_NONE;
                            slot.release();
                            slot.print_timings();
                            send_final_response(slot);
                            continue;
                        }

                        if (slot.embedding)
                        {
                            // this prompt is too large to process - discard it
                            if (slot.n_prompt_tokens > n_ubatch)
                            {
                                slot.state = SLOT_STATE_PROCESSING;
                                slot.command = SLOT_COMMAND_NONE;
                                slot.release();
                                send_error(slot, "input is too large to process. increase the physical batch size",
                                           ERROR_TYPE_SERVER);
                                continue;
                            }
                        }
                        else
                        {
                            if (slot.params.n_keep < 0)
                            {
                                slot.params.n_keep = slot.n_prompt_tokens;
                            }
                            slot.params.n_keep = std::min(slot.n_ctx - 4, slot.params.n_keep);

                            // if input prompt is too big, truncate it (if group attention self-extend is disabled)
                            if (slot.ga_n == 1 && slot.n_prompt_tokens >= slot.n_ctx)
                            {
                                const int n_left = slot.n_ctx - slot.params.n_keep;

                                const int n_block_size = n_left / 2;
                                const int erased_blocks =
                                    (slot.n_prompt_tokens - slot.params.n_keep - n_block_size) / n_block_size;

                                std::vector<llama_token> new_tokens(prompt_tokens.begin(),
                                                                    prompt_tokens.begin() + slot.params.n_keep);

                                new_tokens.insert(new_tokens.end(),
                                                  prompt_tokens.begin() + slot.params.n_keep +
                                                      erased_blocks * n_block_size,
                                                  prompt_tokens.end());

                                prompt_tokens = std::move(new_tokens);

                                slot.truncated = true;
                                slot.n_prompt_tokens = prompt_tokens.size();

                                LOG_VERBOSE("input truncated",
                                            {
                                                {"id_slot", slot.id},
                                                {"id_task", slot.id_task},
                                                {"n_ctx", slot.n_ctx},
                                                {"n_keep", slot.params.n_keep},
                                                {"n_left", n_left},
                                                {"n_prompt_tokens", slot.n_prompt_tokens},
                                                {"prompt_tokens",
                                                 tokens_to_str(ctx, prompt_tokens.cbegin(), prompt_tokens.cend())},
                                            });

                                GGML_ASSERT(slot.n_prompt_tokens < slot.n_ctx);
                            }

                            llama_sampling_reset(slot.ctx_sampling);

                            if (!slot.params.cache_prompt)
                            {
                                slot.n_past_se = 0;
                                slot.ga_i = 0;
                            }
                            else
                            {
                                GGML_ASSERT(slot.ga_n == 1);

                                // reuse any previously computed tokens that are common with the new prompt
                                slot.n_past = common_part(slot.cache_tokens, prompt_tokens);

                                // push the prompt into the sampling context (do not apply grammar)
                                for (int i = 0; i < slot.n_past; ++i)
                                {
                                    llama_sampling_accept(slot.ctx_sampling, ctx, slot.cache_tokens[i], false);
                                }
                            }
                        }

                        if (slot.n_past == slot.n_prompt_tokens && slot.n_past > 0)
                        {
                            // we have to evaluate at least 1 token to generate logits.
                            LOG_INFO("we have to evaluate at least 1 token to generate logits",
                                     {{"id_slot", slot.id}, {"id_task", slot.id_task}});

                            slot.n_past--;
                            if (slot.ga_i > 0)
                            {
                                slot.n_past_se--;
                            }
                        }

                        slot.n_prompt_tokens_processed = 0;
                    }

                    if (slot.embedding)
                    {
                        // cannot fit the prompt in the current batch - will try next iter
                        if (batch.n_tokens + slot.n_prompt_tokens > n_batch)
                        {
                            continue;
                        }
                    }

                    // check that we are in the right batch_type, if not defer the slot
                    bool slot_type = slot.embedding ? 1 : 0;
                    if (batch_type == -1)
                    {
                        batch_type = slot_type;
                    }
                    else if (batch_type != slot_type)
                    {
                        continue;
                    }

                    // keep only the common part
                    int p0 = (int)system_tokens.size() + slot.n_past;
                    if (!llama_kv_cache_seq_rm(ctx, slot.id + 1, p0, -1))
                    {
                        // could not partially delete (likely using a non-Transformer model)
                        llama_kv_cache_seq_rm(ctx, slot.id + 1, -1, -1);

                        p0 = (int)system_tokens.size();
                        if (p0 != 0)
                        {
                            // copy over the system prompt when there is one
                            llama_kv_cache_seq_cp(ctx, 0, slot.id + 1, -1, -1);
                        }

                        // there is no common part left (except for the system prompt)
                        slot.n_past = 0;
                        slot.n_past_se = 0;
                        slot.ga_i = 0;
                        // TODO: is the system prompt ever in the sampling context?
                        llama_sampling_reset(slot.ctx_sampling);
                    }

                    // remove the non-common part from the cache
                    slot.cache_tokens.resize(slot.n_past);

                    LOG_INFO("kv cache rm [p0, end)", {{"id_slot", slot.id}, {"id_task", slot.id_task}, {"p0", p0}});

                    int32_t slot_npast = slot.n_past_se > 0 ? slot.n_past_se : slot.n_past;

                    int32_t ga_i = slot.ga_i;
                    int32_t ga_n = slot.ga_n;
                    int32_t ga_w = slot.ga_w;

                    // add prompt tokens for processing in the current batch
                    // TODO: the self-extend stuff here is a mess - simplify and/or abstract it somehow
                    for (; slot.n_past < slot.n_prompt_tokens && batch.n_tokens < n_batch; ++slot.n_past)
                    {
                        if (slot.ga_n != 1)
                        {
                            while (slot_npast >= ga_i + ga_w)
                            {
                                const int bd = (ga_w / ga_n) * (ga_n - 1);
                                slot_npast -= bd;
                                ga_i += ga_w / ga_n;
                            }
                        }

                        llama_batch_add(batch, prompt_tokens[slot.n_past], system_tokens.size() + slot_npast,
                                        {slot.id + 1}, false);

                        if (slot.params.cache_prompt)
                        {
                            slot.cache_tokens.push_back(prompt_tokens[slot.n_past]);
                        }

                        slot.n_prompt_tokens_processed++;
                        slot_npast++;
                    }

                    LOG_VERBOSE("prompt processing progress",
                                {
                                    {"id_slot", slot.id},
                                    {"n_past", slot.n_past},
                                    {"n_ctx", n_ctx},
                                    {"n_tokens", batch.n_tokens},
                                    {"progress", (float)slot.n_prompt_tokens_processed / slot.n_prompt_tokens},
                                });

                    // entire prompt has been processed - start decoding new tokens
                    if (slot.n_past == slot.n_prompt_tokens)
                    {
                        slot.state = SLOT_STATE_PROCESSING;
                        slot.command = SLOT_COMMAND_NONE;

                        GGML_ASSERT(batch.n_tokens > 0);

                        // extract the logits only for the last token
                        batch.logits[batch.n_tokens - 1] = true;

                        slot.n_decoded = 0;
                        slot.i_batch = batch.n_tokens - 1;

                        LOG_VERBOSE("prompt done", {
                                                       {"id_slot", slot.id},
                                                       {"n_past", slot.n_past},
                                                       {"n_ctx", n_ctx},
                                                       {"n_tokens", batch.n_tokens},
                                                   });
                    }
                }

                if (batch.n_tokens >= n_batch)
                {
                    break;
                }
            }
        }

        if (batch.n_tokens == 0)
        {
            LOG_VERBOSE("no tokens to decode", {});
            return;
        }

        LOG_VERBOSE("decoding batch", {
                                          {"n_tokens", batch.n_tokens},
                                      });

        // make sure we're in the right embedding mode
        llama_set_embeddings(ctx, batch_type == 1);

        // process the created batch of tokens
        for (int32_t i = 0; i < batch.n_tokens; i += n_batch)
        {
            const int32_t n_tokens = std::min(n_batch, batch.n_tokens - i);

            for (auto &slot : slots)
            {
                if (slot.ga_n != 1)
                {
                    // context extension via Self-Extend
                    // TODO: simplify and/or abstract this
                    while (slot.n_past_se >= slot.ga_i + slot.ga_w)
                    {
                        const int ib = (slot.ga_n * slot.ga_i) / slot.ga_w;
                        const int bd = (slot.ga_w / slot.ga_n) * (slot.ga_n - 1);
                        const int dd = (slot.ga_w / slot.ga_n) - ib * bd - slot.ga_w;

                        LOG_TEE("\n");
                        LOG_TEE("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", slot.ga_i, slot.n_past_se, ib * bd,
                                slot.ga_i + ib * bd, slot.n_past_se + ib * bd);
                        LOG_TEE("div:   [%6d, %6d] / %6d -> [%6d, %6d]\n", slot.ga_i + ib * bd,
                                slot.ga_i + ib * bd + slot.ga_w, slot.ga_n, (slot.ga_i + ib * bd) / slot.ga_n,
                                (slot.ga_i + ib * bd + slot.ga_w) / slot.ga_n);
                        LOG_TEE("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", slot.ga_i + ib * bd + slot.ga_w,
                                slot.n_past_se + ib * bd, dd, slot.ga_i + ib * bd + slot.ga_w + dd,
                                slot.n_past_se + ib * bd + dd);

                        llama_kv_cache_seq_add(ctx, slot.id + 1, slot.ga_i, slot.n_past_se, ib * bd);
                        llama_kv_cache_seq_div(ctx, slot.id + 1, slot.ga_i + ib * bd, slot.ga_i + ib * bd + slot.ga_w,
                                               slot.ga_n);
                        llama_kv_cache_seq_add(ctx, slot.id + 1, slot.ga_i + ib * bd + slot.ga_w,
                                               slot.n_past_se + ib * bd, dd);

                        slot.n_past_se -= bd;

                        slot.ga_i += slot.ga_w / slot.ga_n;

                        LOG_TEE("\nn_past_old = %d, n_past = %d, ga_i = %d\n\n", slot.n_past_se + bd, slot.n_past_se,
                                slot.ga_i);
                    }

                    slot.n_past_se += n_tokens;
                }
            }

            llama_batch batch_view = {
                n_tokens,
                batch.token + i,
                nullptr,
                batch.pos + i,
                batch.n_seq_id + i,
                batch.seq_id + i,
                batch.logits + i,
                0,
                0,
                0, // unused
            };

            const int ret = llama_decode(ctx, batch_view);

            if (ret != 0)
            {
                if (n_batch == 1 || ret < 0)
                {
                    // if you get here, it means the KV cache is full - try increasing it via the context size
                    LOG_ERROR("failed to decode the batch: KV cache is full - try increasing it via the context size",
                              {
                                  {"i", i},
                                  {"n_batch", ret},
                                  {"ret", ret},
                              });
                    for (auto &slot : slots)
                    {
                        slot.state = SLOT_STATE_PROCESSING;
                        slot.command = SLOT_COMMAND_NONE;
                        slot.release();
                        send_error(slot, "Input prompt is too big compared to KV size. Please try increasing KV size.");
                    }
                    break; // break loop of n_batch
                }

                // retry with half the batch size to try to find a free slot in the KV cache
                n_batch /= 2;
                i -= n_batch;

                LOG_WARNING("failed to find free space in the KV cache, retrying with smaller batch size - try "
                            "increasing it via the context size or enable defragmentation",
                            {
                                {"i", i},
                                {"n_batch", n_batch},
                                {"ret", ret},
                            });

                continue; // continue loop of n_batch
            }

            for (auto &slot : slots)
            {
                if (slot.state != SLOT_STATE_PROCESSING || slot.i_batch < (int)i || slot.i_batch >= (int)(i + n_tokens))
                {
                    continue; // continue loop of slots
                }

                // prompt evaluated for embedding
                if (slot.embedding)
                {
                    send_embedding(slot, batch_view);
                    slot.release();
                    slot.i_batch = -1;
                    continue; // continue loop of slots
                }

                completion_token_output result;
                const llama_token id = llama_sampling_sample(slot.ctx_sampling, ctx, NULL, slot.i_batch - i);

                llama_sampling_accept(slot.ctx_sampling, ctx, id, true);

                slot.n_decoded += 1;
                if (slot.n_decoded == 1)
                {
                    slot.t_start_generation = ggml_time_us();
                    slot.t_prompt_processing = (slot.t_start_generation - slot.t_start_process_prompt) / 1e3;
                    metrics.on_prompt_eval(slot);
                }

                llama_token_data_array cur_p = {slot.ctx_sampling->cur.data(), slot.ctx_sampling->cur.size(), false};
                result.tok = id;

                const size_t n_probs = std::min(cur_p.size, (size_t)slot.sparams.n_probs);
                if (n_probs > 0)
                {
                    const size_t n_valid = slot.ctx_sampling->n_valid;

                    // Make sure at least n_probs top tokens are at the front of the vector:
                    if (slot.sparams.temp == 0.0f && n_probs > n_valid)
                    {
                        llama_sample_top_k(ctx, &cur_p, n_probs, 0);
                    }

                    if (slot.sparams.temp == 0.0f)
                    {
                        // With greedy sampling the probabilities have possibly not been calculated.
                        for (size_t i = 0; i < n_probs; ++i)
                        {
                            result.probs.push_back({cur_p.data[i].id, i == 0 ? 1.0f : 0.0f});
                        }
                    }
                    else
                    {
                        for (size_t i = 0; i < n_probs; ++i)
                        {
                            result.probs.push_back({
                                cur_p.data[i].id,
                                i >= n_valid
                                    ? 0.0f
                                    : cur_p.data[i].p // Tokens filtered out due to e.g. top_k have 0 probability.
                            });
                        }
                    }
                }

                if (!process_token(result, slot))
                {
                    slot.release();
                    slot.print_timings();
                    send_final_response(slot);
                    metrics.on_prediction(slot);
                }

                slot.i_batch = -1;
            }
        }

        LOG_VERBOSE("run slots completed", {});
    }

    json model_meta() const
    {
        return json{
            {"vocab_type", llama_vocab_type(model)},   {"n_vocab", llama_n_vocab(model)},
            {"n_ctx_train", llama_n_ctx_train(model)}, {"n_embd", llama_n_embd(model)},
            {"n_params", llama_model_n_params(model)}, {"size", llama_model_size(model)},
        };
    }
};

// parse the given jparams (see de.kherud.llama.args.ModelParameters#toString()) from JSON to the required C++ struct.
static void server_params_parse(json jparams, gpt_params &params)
{
    gpt_params default_params;

    params.seed = json_value(jparams, "seed", default_params.seed);
    params.n_threads = json_value(jparams, "n_threads", default_params.n_threads);
    params.n_threads_draft = json_value(jparams, "n_threads_draft", default_params.n_threads_draft);
    params.n_threads_batch = json_value(jparams, "n_threads_batch", default_params.n_threads_batch);
    params.n_threads_batch_draft = json_value(jparams, "n_threads_batch_draft", default_params.n_threads_batch_draft);
    params.n_predict = json_value(jparams, "n_predict", default_params.n_predict);
    params.n_ctx = json_value(jparams, "n_ctx", default_params.n_ctx);
    params.n_batch = json_value(jparams, "n_batch", default_params.n_batch);
    params.n_ubatch = json_value(jparams, "n_ubatch", default_params.n_ubatch);
    params.n_keep = json_value(jparams, "n_keep", default_params.n_keep);
    params.n_draft = json_value(jparams, "n_draft", default_params.n_draft);
    params.n_chunks = json_value(jparams, "n_chunks", default_params.n_chunks);
    params.n_parallel = json_value(jparams, "n_parallel", default_params.n_parallel);
    params.n_sequences = json_value(jparams, "n_sequences", default_params.n_sequences);
    params.p_split = json_value(jparams, "p_split", default_params.p_split);
    params.grp_attn_n = json_value(jparams, "grp_attn_n", default_params.grp_attn_n);
    params.grp_attn_w = json_value(jparams, "grp_attn_w", default_params.grp_attn_w);
    params.n_print = json_value(jparams, "n_print", default_params.n_print);
    params.rope_freq_base = json_value(jparams, "rope_freq_base", default_params.rope_freq_base);
    params.rope_freq_scale = json_value(jparams, "rope_freq_scale", default_params.rope_freq_scale);
    params.yarn_ext_factor = json_value(jparams, "yarn_ext_factor", default_params.yarn_ext_factor);
    params.yarn_attn_factor = json_value(jparams, "yarn_attn_factor", default_params.yarn_attn_factor);
    params.yarn_beta_fast = json_value(jparams, "yarn_beta_fast", default_params.yarn_beta_fast);
    params.yarn_beta_slow = json_value(jparams, "yarn_beta_slow", default_params.yarn_beta_slow);
    params.yarn_orig_ctx = json_value(jparams, "yarn_orig_ctx", default_params.yarn_orig_ctx);
    params.defrag_thold = json_value(jparams, "defrag_thold", default_params.defrag_thold);
    params.numa = json_value(jparams, "numa", default_params.numa);
    params.rope_scaling_type = json_value(jparams, "rope_scaling_type", default_params.rope_scaling_type);
    params.pooling_type = json_value(jparams, "pooling_type", default_params.pooling_type);
    params.model = json_value(jparams, "model", default_params.model);
    params.model_draft = json_value(jparams, "model_draft", default_params.model_draft);
    params.model_alias = json_value(jparams, "model_alias", default_params.model_alias);
    params.model_url = json_value(jparams, "model_url", default_params.model_url);
    params.hf_repo = json_value(jparams, "hf_repo", default_params.hf_repo);
    params.hf_file = json_value(jparams, "hf_file", default_params.hf_file);
    params.prompt = json_value(jparams, "prompt", default_params.prompt);
    params.prompt_file = json_value(jparams, "prompt_file", default_params.prompt_file);
    params.path_prompt_cache = json_value(jparams, "path_prompt_cache", default_params.path_prompt_cache);
    params.input_prefix = json_value(jparams, "input_prefix", default_params.input_prefix);
    params.input_suffix = json_value(jparams, "input_suffix", default_params.input_suffix);
    params.antiprompt = json_value(jparams, "antiprompt", default_params.antiprompt);
    params.lookup_cache_static = json_value(jparams, "lookup_cache_static", default_params.lookup_cache_static);
    params.lookup_cache_dynamic = json_value(jparams, "lookup_cache_dynamic", default_params.lookup_cache_dynamic);
    params.logits_file = json_value(jparams, "logits_file", default_params.logits_file);
    params.lora_adapter = json_value(jparams, "lora_adapter", default_params.lora_adapter);
    params.embedding = json_value(jparams, "embedding", default_params.embedding);
    params.escape = json_value(jparams, "escape", default_params.escape);
    params.cont_batching = json_value(jparams, "cont_batching", default_params.cont_batching);
    params.flash_attn = json_value(jparams, "flash_attn", default_params.flash_attn);
    params.input_prefix_bos = json_value(jparams, "input_prefix_bos", default_params.input_prefix_bos);
    params.ignore_eos = json_value(jparams, "ignore_eos", default_params.ignore_eos);
    params.use_mmap = json_value(jparams, "use_mmap", default_params.use_mmap);
    params.use_mlock = json_value(jparams, "use_mlock", default_params.use_mlock);
    params.no_kv_offload = json_value(jparams, "no_kv_offload", default_params.no_kv_offload);
    params.system_prompt = json_value(jparams, "system_prompt", default_params.system_prompt);
    params.chat_template = json_value(jparams, "chat_template", default_params.chat_template);

    if (jparams.contains("n_gpu_layers"))
    {
        if (llama_supports_gpu_offload())
        {
            params.n_gpu_layers = json_value(jparams, "n_gpu_layers", default_params.n_gpu_layers);
            params.n_gpu_layers_draft = json_value(jparams, "n_gpu_layers_draft", default_params.n_gpu_layers_draft);
        }
        else
        {
            LOG_WARNING("Not compiled with GPU offload support, --n-gpu-layers option will be ignored. "
                        "See main README.md for information on enabling GPU BLAS support",
                        {{"n_gpu_layers", params.n_gpu_layers}});
        }
    }

    if (jparams.contains("split_mode"))
    {
        params.split_mode = json_value(jparams, "split_mode", default_params.split_mode);
// todo: the definition checks here currently don't work due to cmake visibility reasons
#ifndef GGML_USE_CUDA
        fprintf(stderr, "warning: llama.cpp was compiled without CUDA. Setting the split mode has no effect.\n");
#endif
    }

    if (jparams.contains("tensor_split"))
    {
#if defined(GGML_USE_CUDA) || defined(GGML_USE_SYCL)
        std::vector<float> tensor_split = jparams["tensor_split"].get<std::vector<float>>();
        GGML_ASSERT(tensor_split.size() <= llama_max_devices());

        for (size_t i_device = 0; i_device < llama_max_devices(); ++i_device)
        {
            if (i_device < tensor_split.size())
            {
                params.tensor_split[i_device] = tensor_split.at(i_device);
            }
            else
            {
                params.tensor_split[i_device] = 0.0f;
            }
        }
#else
        LOG_WARNING("llama.cpp was compiled without CUDA. It is not possible to set a tensor split.\n", {});
#endif // GGML_USE_CUDA
    }

    if (jparams.contains("main_gpu"))
    {
#if defined(GGML_USE_CUDA) || defined(GGML_USE_SYCL)
        params.main_gpu = json_value(jparams, "main_gpu", default_params.main_gpu);
#else
        LOG_WARNING("llama.cpp was compiled without CUDA. It is not possible to set a main GPU.", {});
#endif
    }

    gpt_params_handle_model_default(params);
}
